Candida Ferreira

Gene Expression Programming

Mathematical Modeling by an Artificial Intelligence

• Gebundenes Buch

Produktbeschreibung

Cândida Ferreira thoroughly describes the basic ideas of gene expression programming (GEP) and numerous modifications to this powerful new algorithm. This monograph provides all the implementation details of GEP so that anyone with elementary programming skills will be able to implement it themselves. The book also includes a self-contained introduction to this new exciting field of computational intelligence, including several new algorithms for decision tree induction, data mining, classifier systems, function finding, polynomial induction, times series prediction, evolution of linking functions, automatically defined functions, parameter optimization, logic synthesis, combinatorial optimization, and complete neural network induction. The book also discusses some important and controversial evolutionary topics that might be refreshing to both evolutionary computer scientists and biologists.

This second edition has been substantially revised and extended with five newchapters, including a new chapter describing two new algorithms for inducing decision trees with nominal and numeric/mixed attributes.

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Produktdetails

• Studies in Computational Intelligence 21
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-32796-7
• 2nd ed. 2006
• Seitenzahl: 504
• Erscheinungstermin: 24. Mai 2006
• Englisch
• Abmessung: 241mm x 160mm x 33mm
• Gewicht: 864g
• ISBN-13: 9783540327967
• ISBN-10: 3540327967
• Artikelnr.: 20801576

Autorenporträt

Candida Ferreira, Gepsoft Ltd., Bristol, UK

Inhaltsangabe

Introduction: The Biological Perspective.- The Entities of Gene Expression Programming.- The Basic Gene Expression Algorithm.- The Basic GEA in Problem Solving.- Numerical Constants and the GEP-RNC Algorithm.- Automatically Defined Functions in Problem Solving.- Polynomial Induction and Time Series Prediction.- Parameter Optimization.- Decision Tree Induction.- Design of Neural Networks.- Combinatorial Optimization.- Evolutionary Studies.